######################################################################
+def all_properties(height, width, nb_squares, square_i, square_j, square_c):
+ s = [ ]
+
+ for r, c in [ (k, color_names[square_c[k]]) for k in range(nb_squares) ]:
+ s += [ f'there is {c}' ]
+
+ if square_i[r] >= height - height//3: s += [ f'{c} bottom' ]
+ if square_i[r] < height//3: s += [ f'{c} top' ]
+ if square_j[r] >= width - width//3: s += [ f'{c} right' ]
+ if square_j[r] < width//3: s += [ f'{c} left' ]
+
+ for t, d in [ (k, color_names[square_c[k]]) for k in range(nb_squares) ]:
+ if square_i[r] > square_i[t]: s += [ f'{c} below {d}' ]
+ if square_i[r] < square_i[t]: s += [ f'{c} above {d}' ]
+ if square_j[r] > square_j[t]: s += [ f'{c} right of {d}' ]
+ if square_j[r] < square_j[t]: s += [ f'{c} left of {d}' ]
+
+ return s
+
+######################################################################
+
def generate(nb, height = 6, width = 8,
- max_nb_squares = 5, max_nb_statements = 10,
+ max_nb_squares = 5, max_nb_properties = 10,
many_colors = False):
nb_colors = len(color_tokens) - 1 if many_colors else max_nb_squares
img = [ 0 ] * height * width
for k in range(nb_squares): img[square_position[k]] = square_c[k]
- # generates all the true relations
+ # generates all the true properties
- s = [ ]
+ s = all_properties(height, width, nb_squares, square_i, square_j, square_c)
- for r, c in [ (k, color_names[square_c[k]]) for k in range(nb_squares) ]:
- s += [ f'there is {c}' ]
+ # pick at most max_nb_properties at random
- if square_i[r] >= height - height//3: s += [ f'{c} bottom' ]
- if square_i[r] < height//3: s += [ f'{c} top' ]
- if square_j[r] >= width - width//3: s += [ f'{c} right' ]
- if square_j[r] < width//3: s += [ f'{c} left' ]
-
- for t, d in [ (k, color_names[square_c[k]]) for k in range(nb_squares) ]:
- if square_i[r] > square_i[t]: s += [ f'{c} below {d}' ]
- if square_i[r] < square_i[t]: s += [ f'{c} above {d}' ]
- if square_j[r] > square_j[t]: s += [ f'{c} right of {d}' ]
- if square_j[r] < square_j[t]: s += [ f'{c} left of {d}' ]
-
- # pick at most max_nb_statements at random
-
- nb_statements = torch.randint(max_nb_statements, (1,)) + 1
- s = ' <sep> '.join([ s[k] for k in torch.randperm(len(s))[:nb_statements] ] )
+ nb_properties = torch.randint(max_nb_properties, (1,)) + 1
+ s = ' <sep> '.join([ s[k] for k in torch.randperm(len(s))[:nb_properties] ] )
s += ' <img> ' + ' '.join([ f'{color_names[n]}' for n in img ])
descr += [ s ]
def descr2img(descr, height = 6, width = 8):
+ if type(descr) == list:
+ return torch.cat([ descr2img(d) for d in descr ], 0)
+
def token2color(t):
try:
return color_tokens[t]
except KeyError:
return [ 128, 128, 128 ]
- def img_descr(x):
- u = x.split('<img>', 1)
- return u[1] if len(u) > 1 else ''
-
- img = torch.full((len(descr), 3, height, width), 255)
- d = [ img_descr(x) for x in descr ]
- d = [ u.strip().split(' ')[:height * width] for u in d ]
- d = [ u + [ '<unk>' ] * (height * width - len(u)) for u in d ]
- d = [ [ token2color(t) for t in u ] for u in d ]
- img = torch.tensor(d).permute(0, 2, 1)
- img = img.reshape(img.size(0), 3, height, width)
+ d = descr.split('<img>', 1)
+ d = d[-1] if len(d) > 1 else ''
+ d = d.strip().split(' ')[:height * width]
+ d = d + [ '<unk>' ] * (height * width - len(d))
+ d = [ token2color(t) for t in d ]
+ img = torch.tensor(d).permute(1, 0)
+ img = img.reshape(1, 3, height, width)
return img
if __name__ == '__main__':
descr = generate(nb = 5)
- for d in descr:
- print(d)
- print()
- img = descr2img(descr)
- print(img.size())
+ with open('picoclvr_example.txt', 'w') as f:
+ for d in descr:
+ f.write(f'{d}\n\n')
+ img = descr2img(descr)
torchvision.utils.save_image(img / 255.,
'picoclvr_example.png', nrow = 16, pad_value = 0.8)